---
library_name: peft
license: llama3.2
base_model: NousResearch/Llama-3.2-1B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: a799937b-9418-4d0f-927f-7ec8073d086a
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>

axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: NousResearch/Llama-3.2-1B
bf16: true
chat_template: llama3
data_processes: 54
dataset_prepared_path: null
datasets:
- data_files:
  - a1fd3b8799300f0a_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/a1fd3b8799300f0a_train_data.json
  type:
    field_instruction: constraints
    field_output: prompt
    format: '{instruction}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
device_map: auto
distributed_training:
  multi_gpu: true
  num_gpus: 2
do_eval: true
early_stopping_patience: 4
eval_batch_size: 8
eval_max_new_tokens: 128
eval_steps: 150
eval_table_size: null
evals_per_epoch: null
flash_attention: true
fp16: false
fsdp:
- full_shard
fsdp_config:
  activation_checkpointing: false
  backward_prefetch: BACKWARD_POST
  forward_prefetch: FORWARD_POST
  fsdp_min_num_params: 1000000000
  limit_all_gathers: true
  mixed_precision: bf16
  sharding_strategy: FULL_SHARD
gradient_accumulation_steps: 2
gradient_checkpointing: true
group_by_length: true
hub_model_id: cimol/a799937b-9418-4d0f-927f-7ec8073d086a
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 7.0e-05
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.04
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
lr_scheduler_warmup_steps: 100
max_grad_norm: 1.0
max_memory:
  0: 80GB
  1: 80GB
max_steps: 750
micro_batch_size: 8
mlflow_experiment_name: /tmp/a1fd3b8799300f0a_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optim_args:
  adam_beta1: 0.9
  adam_beta2: 0.95
  adam_epsilon: 1e-8
optimizer: adamw_torch
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 300
saves_per_epoch: null
seed: 17333
sequence_len: 1024
special_tokens:
  pad_token: <|end_of_text|>
strict: false
tf32: true
tokenizer_type: AutoTokenizer
total_train_batch_size: 32
train_batch_size: 16
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 7ba6d308-f32a-4ef8-86b8-9e9b314b6fb0
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 7ba6d308-f32a-4ef8-86b8-9e9b314b6fb0
warmup_steps: 100
weight_decay: 0.0
xformers_attention: null

```

</details><br>

# a799937b-9418-4d0f-927f-7ec8073d086a

This model is a fine-tuned version of [NousResearch/Llama-3.2-1B](https://huggingface.co/NousResearch/Llama-3.2-1B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5127

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 7e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 17333
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- total_eval_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.95,adam_epsilon=1e-8
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- training_steps: 750

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 2.0615        | 0.0017 | 1    | 2.8681          |
| 1.9878        | 0.2564 | 150  | 1.7228          |
| 1.8938        | 0.5128 | 300  | 1.5987          |
| 1.8665        | 0.7692 | 450  | 1.5405          |
| 1.4281        | 1.0256 | 600  | 1.5162          |
| 1.4781        | 1.2821 | 750  | 1.5127          |


### Framework versions

- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1